The approaches described in this section could be pursued, but are not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated herein, the approaches described in this section are not prior art to the claims in this application and are not admitted to be prior art by inclusion in this section. Furthermore, all embodiments are not necessarily intended to solve all or even any of the problems brought forward in this section.
The determination of a lithology map is important for modeling petroleum, oil and/or gas reservoirs. Thus, this determination is a key point for geologists.
After a discovery of a reservoir, petroleum engineers and/or geologists seek to build a clear picture of the accumulation. Most of the time, the first stage is to conduct a seismic survey to determine the possible size of the trap and to estimate the volume of oil bearing reservoir. Geologists, geophysicists and reservoir engineers work together to build a model which enables simulations (for instance, simulation of the flow of fluids in the reservoir) leading to improved estimates of oil/gas reserves. Thus, it is very important to have accurate lithology map for this estimation.
To constrain a lithology model to seismic, a classical approach may be to derive soft probabilities from rock physics and statistical analysis of geophysical properties (inverted acoustic impedances or pseudo V-Clay). Usually a relevant analysis can be performed at log scale; but it is not obvious to transfer this information at the scale of a grid cell for geo-modeling prediction. Up-scaling issues lead usually to less contrasted soft probabilities and the direct use of these probabilities in geostatistical processes are often deceiving.
Some transformations may be suggested to use these seismic soft probabilities but these transformations are usually subjective and case dependant.
Other suggestions may be to combine soft probabilities with probabilities interpreted form well data and geological analysis but debates may be raised:                Which combination is to be used? Is it a convex or concave combination?        Which is the most representative probability? Is it geological probabilities because they are based on hard data or geophysical probabilities because they are more representative of the entire reservoir?        
For all these reasons, there is a need to conciliate geological and geophysical point of views in order to provide an additional tool focused on estimation of lithology instead of classical simulation process.